Systems and methods for determining oxygen saturation

ABSTRACT

According to embodiments, techniques for using continuous wavelet transforms and spectral transforms to determine oxygen saturation from photoplethysmographic (PPG) signals are disclosed. According to embodiments, a first and a second PPG signals may be received. A spectral transform of the first and the second PPG signals may be performed to produce a first and a second spectral transformed signals. A frequency region associated with a pulse rate of the PPG signals may be identified from the first and the second spectral transformed signals. According to embodiments, a continuous wavelet transform of the first and the second PPG signals may be performed at a scale corresponding to the identified frequency region to produce a first and a second wavelet transformed signals. The oxygen saturation may be determined based at least in part upon the wavelet transformed signals.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.12/249,068 entitled “Systems and Methods for Determining OxygenSaturation,” filed Oct. 10, 2008, which claims the benefit of U.S.Provisional Application No. 61/081,016, filed Jul. 15, 2008, both ofwhich are hereby incorporated by reference herein in their entireties.

SUMMARY

The present disclosure relates to signal processing and, moreparticularly, the present disclosure relates to using continuous wavelettransforms and spectral transforms to determine oxygen saturation from aphotoplethysmographic (PPG) signal.

The known advantages of the Continuous Wavelet Transform (CWT), inherentresolution in both scale and time, may be advantageous in identifyingand characterizing features within a signal. Oxygen saturation may becalculated and tracked in time based at least in part on continuouswavelet transforms of a first PPG signal and a second PPG signal. Oxygensaturation may also be calculated using spectral techniques. Inaccordance with the present disclosure, oxygen saturation may becalculated most accurately and efficiently by using the strengths of theCWT in conjunction with the spectral transforms.

Oxygen saturation may be calculated using both the spectral transformand CWT techniques with the two results combined by, for example takingan average or weighted average to obtain an improved value for thecalculated pulse rate. Alternatively, oxygen saturation may becalculated using both the spectral and CWT techniques and one of theresults may be selected such as the one which is closest to an expected(e.g., historical) value to obtain an improved value for the calculatedoxygen saturation.

In some embodiments, both the spectral and CWT techniques may be usedtogether to calculate oxygen saturation. For example, a spectraltransform may be used to quickly identify frequency components of PPGssignals frequency regions of a signal likely to be the pulse rate. TheCWT may then focus on these regions to calculate oxygen saturation. Thatis, the spectral transforms may be employed, using, for example,standard peak-picking techniques (e.g., selecting maxima turningpoints), to identify nominal frequency(s) of interest for identificationof pulse rate. The CWT may then be performed at scales withcharacteristic frequency corresponding to these frequencies of interest.The maxima ridges in the scalogram proximal to these frequencies ofinterest may then be used to calculate oxygen saturation. The CWTtechnique may provide a more accurate calculation of the oxygensaturation to its ability to track changes in pulse rate (changes in thefrequency of interest) through time and its ability to ignore regions ofscalogram associated with temporally discrete artifacts (e.g., spikes).Both these features are due to the CWT's ability to better resolve inthe time domain when compared to spectral averaging techniques.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other features of the present disclosure, its nature andvarious advantages will be more apparent upon consideration of thefollowing detailed description, taken in conjunction with theaccompanying drawings in which:

FIG. 1 shows an illustrative pulse oximetry system in accordance with anembodiment;

FIG. 2 is a block diagram of the illustrative pulse oximetry system ofFIG. 1 coupled to a patient in accordance with an embodiment;

FIGS. 3(a) and 3(b) show illustrative views of a scalogram derived froma PPG signal in accordance with an embodiment;

FIG. 3(c) shows an illustrative scalogram derived from a signalcontaining two pertinent components in accordance with an embodiment;

FIG. 3(d) shows an illustrative schematic of signals associated with aridge in FIG. 3(c) and illustrative schematics of a further waveletdecomposition of these newly derived signals in accordance with anembodiment;

FIGS. 3(e) and 3(f) are flow charts of illustrative steps involved inperforming an inverse continuous wavelet transform in accordance withembodiments;

FIG. 4 is a block diagram of an illustrative continuous waveletprocessing system in accordance with some embodiments;

FIG. 5 shows an illustrative schematic of a system for determiningoxygen saturation from PPG signals in accordance with some embodiments;

FIG. 6 is a flowchart of an illustrative process for determining oxygensaturation from PPG signals using continuous wavelet transforms andspectral transforms in accordance with some embodiments; and

FIG. 7 is a flowchart of another illustrative process for determiningoxygen saturation from PPG signals using continuous wavelet transformsand spectral transforms in accordance with some embodiments.

DETAILED DESCRIPTION

An oximeter is a medical device that may determine the oxygen saturationof the blood. One common type of oximeter is a pulse oximeter, which mayindirectly measure the oxygen saturation of a patient's blood (asopposed to measuring oxygen saturation directly by analyzing a bloodsample taken from the patient) and changes in blood volume in the skin.Ancillary to the blood oxygen saturation measurement, pulse oximetersmay also be used to measure the pulse rate of the patient. Pulseoximeters typically measure and display various blood flowcharacteristics including, but not limited to, the oxygen saturation ofhemoglobin in arterial blood.

An oximeter may include a light sensor that is placed at a site on apatient, typically a fingertip, toe, forehead or earlobe, or in the caseof a neonate, across a foot. The oximeter may pass light using a lightsource through blood perfused tissue and photoelectrically sense theabsorption of light in the tissue. For example, the oximeter may measurethe intensity of light that is received at the light sensor as afunction of time. A signal representing light intensity versus time or amathematical manipulation of this signal (e.g., a scaled versionthereof, a log taken thereof, a scaled version of a log taken thereof,etc.) may be referred to as the photoplethysmograph (PPG) signal. Inaddition, the term “PPG signal,” as used herein, may also refer to anabsorption signal (i.e., representing the amount of light absorbed bythe tissue) or any suitable mathematical manipulation thereof. The lightintensity or the amount of light absorbed may then be used to calculatethe amount of the blood constituent (e.g., oxyhemoglobin) being measuredas well as the pulse rate and when each individual pulse occurs.

The light passed through the tissue is selected to be of one or morewavelengths that are absorbed by the blood in an amount representativeof the amount of the blood constituent present in the blood. The amountof light passed through the tissue varies in accordance with thechanging amount of blood constituent in the tissue and the related lightabsorption. Red and infrared wavelengths may be used because it has beenobserved that highly oxygenated blood will absorb relatively less redlight and more infrared light than blood with a lower oxygen saturation.By comparing the intensities of two wavelengths at different points inthe pulse cycle, it is possible to estimate the blood oxygen saturationof hemoglobin in arterial blood.

When the measured blood parameter is the oxygen saturation ofhemoglobin, a convenient starting point assumes a saturation calculationbased on Lambert-Beer's law. The following notation will be used herein:I(λ,t)=I _(o)(λ)exp(−(sβ _(o)(λ)+(1−s)β_(r)(λ))l(t))  (1)where:

-   λ=wavelength;-   t=time;-   I=intensity of light detected;-   I_(o)=intensity of light transmitted;-   s=oxygen saturation;-   β_(o), β_(r)=empirically derived absorption coefficients; and-   1(t)=a combination of concentration and path length from emitter to    detector as a function of time.

The traditional approach measures light absorption at two wavelengths(e.g., red and infrared (IR)), and then calculates saturation by solvingfor the “ratio of ratios” as follows.

-   1. First, the natural logarithm of (1) is taken (“log” will be used    to represent the natural logarithm) for IR and Red    log I=log I _(o)−(sβ _(o)+(1−s)β_(r))l  (2)-   2. (2) is then differentiated with respect to time

$\begin{matrix}{\frac{d\;\log\; I}{d\; t} = {{- \left( {{s\;\beta_{o}} + {\left( {1 - s} \right)\beta_{r}}} \right)}\frac{d\; l}{d\; t}}} & (3)\end{matrix}$

-   3. Red (3) is divided by IR (3)

$\begin{matrix}{\frac{\frac{d\;\log\;{I\left( \lambda_{R} \right)}}{d\; t}}{\frac{d\;\log\;{I\left( \lambda_{IR} \right)}}{d\; t}} = \frac{{s\;{\beta_{o}\left( \lambda_{R} \right)}} + {\left( {1 - s} \right){\beta_{r}\left( \lambda_{R} \right)}}}{{s\;{\beta_{o}\left( \lambda_{IR} \right)}} + {\left( {1 - s} \right){\beta_{r}\left( \lambda_{IR} \right)}}}} & (4)\end{matrix}$

-   4. Solving for s

$s = \frac{{\frac{d\;\log\;{I\left( \lambda_{IR} \right)}}{d\; t}{\beta_{r}\left( \lambda_{R} \right)}} - {\frac{d\;\log\;{I\left( \lambda_{R} \right)}}{d\; t}{\beta_{r}\left( \lambda_{IR} \right)}}}{\begin{matrix}{{\frac{d\;\log\;{I\left( \lambda_{R} \right)}}{d\; t}\left( {{\beta_{o}\left( \lambda_{IR} \right)} - {\beta_{r}\left( \lambda_{IR} \right)}} \right)} -} \\{\frac{d\;\log\;{I\left( \lambda_{IR} \right)}}{d\; t}\left( {{\beta_{o}\left( \lambda_{R} \right)} - {\beta_{r}\left( \lambda_{R} \right)}} \right)}\end{matrix}}$Note in discrete time

$\frac{d\;\log\;{I\left( {\lambda,t} \right)}}{d\; t} \simeq {{\log\;{I\left( {\lambda,t_{2}} \right)}} - {\log\;{I\left( {\lambda,t_{1}} \right)}}}$

-   Using log A−log B=log A/B,

$\frac{d\;\log\;{I\left( {\lambda,t} \right)}}{d\; t} \simeq {\log\left( \frac{I\left( {t_{2},\lambda} \right)}{I\left( {t_{1},\lambda} \right)} \right)}$

-   So, (4) can be rewritten as

$\begin{matrix}{{\frac{\frac{d\;\log\;{I\left( \lambda_{R} \right)}}{d\; t}}{\frac{d\;\log\;{I\left( \lambda_{IR} \right)}}{d\; t}} \simeq \frac{\log\left( \frac{I\left( {t_{1},\lambda_{R}} \right)}{I\left( {t_{2},\lambda_{R}} \right)} \right)}{\log\left( \frac{I\left( {t_{1},\lambda_{IR}} \right)}{I\left( {t_{2},\lambda_{IR}} \right)} \right)}} = R} & (5)\end{matrix}$where R represents the “ratio of ratios.” Solving (4) for s using (5)gives

$s = {\frac{{\beta_{r}\left( \lambda_{R} \right)} - {R\;{\beta_{r}\left( \lambda_{IR} \right)}}}{{R\left( {{\beta_{o}\left( \lambda_{IR} \right)} - {\beta_{r}\left( \lambda_{IR} \right)}} \right)} - {\beta_{o}\left( \lambda_{R} \right)} + {\beta_{r}\left( \lambda_{R} \right)}}.}$

-   From (5), R can be calculated using two points (e.g., PPG maximum    and minimum), or a family of points. One method using a family of    points uses a modified version of (5). Using the relationship

$\begin{matrix}{\frac{d\;\log\; I}{d\; t} = \frac{\frac{d\; I}{d\; t}}{I}} & (6)\end{matrix}$now (5) becomes

$\begin{matrix}\begin{matrix}{\frac{\frac{d\;\log\;{I\left( \lambda_{R} \right)}}{d\; t}}{\frac{d\;\log\;{I\left( \lambda_{IR} \right)}}{d\; t}} \simeq \frac{\frac{{I\left( {t_{2},\lambda_{R}} \right)} - {I\left( {t_{1},\lambda_{R}} \right)}}{I\left( {t_{1},\lambda_{R}} \right)}}{\frac{{I\left( {t_{2},\lambda_{IR}} \right)} - {I\left( {t_{1},\lambda_{IR}} \right)}}{I\left( {t_{1},\lambda_{IR}} \right)}}} \\{= \frac{\left\lbrack {{I\left( {t_{2},\lambda_{R}} \right)} - {I\left( {t_{1},\lambda_{R}} \right)}} \right\rbrack{I\left( {t_{1},\lambda_{IR}} \right)}}{\left\lbrack {{I\left( {t_{2},\lambda_{IR}} \right)} - {I\left( {t_{1},\lambda_{IR}} \right)}} \right\rbrack{I\left( {t_{1},\lambda_{R}} \right)}}} \\{= R}\end{matrix} & (7)\end{matrix}$which defines a cluster of points whose slope of y versus x will give Rwherex(t)=[I(t ₂,λ_(IR))−I(t ₁,λ_(IR))]I(t ₁,λ_(R)) y(t)=[I(t ₂,λ_(R))−I(t₁,λ_(R))]I(t ₁,λ_(IR)) y(t)=Rx(t)  (8)

FIG. 1 is a perspective view of an embodiment of a pulse oximetry system10. System 10 may include a sensor 12 and a pulse oximetry monitor 14.Sensor 12 may include an emitter 16 for emitting light at two or morewavelengths into a patient's tissue. A detector 18 may also be providedin sensor 12 for detecting the light originally from emitter 16 thatemanates from the patient's tissue after passing through the tissue.

According to an embodiment system 10 may include a plurality of sensorsforming a sensor array in lieu of single sensor 12. Each of the sensorsof the sensor array may be a complementary metal oxide semiconductor(CMOS) sensor. Alternatively, each sensor of the array may be chargedcoupled device (CCD) sensor. In another embodiment, the sensor array maybe made up of a combination of CMOS and CCD sensors. The CCD sensor maycomprise a photoactive region and a transmission region for receivingand transmitting data whereas the CMOS sensor may be made up of anintegrated circuit having an array of pixel sensors. Each pixel may havea photodetector and an active amplifier.

According to an embodiment, emitter 16 and detector 18 may be onopposite sides of a digit such as a finger or toe, in which case thelight that is emanating from the tissue has passed completely throughthe digit. In an embodiment, emitter 16 and detector 18 may be arrangedso that light from emitter 16 penetrates the tissue and is reflected bythe tissue into detector 18, such as a sensor designed to obtain pulseoximetry data from a patient's forehead.

In an embodiment, the sensor or sensor array may be connected to anddraw its power from monitor 14 as shown. In another embodiment, thesensor may be wirelessly connected to monitor 14 and include its ownbattery or similar power supply (not shown). Monitor 14 may beconfigured to calculate physiological parameters based at least in parton data received from sensor 12 relating to light emission anddetection. In an alternative embodiment, the calculations may beperformed on the monitoring device itself and the result of the oximetryreading may be passed to monitor 14. Further, monitor 14 may include adisplay 20 configured to display the physiological parameters or otherinformation about the system. In the embodiment shown, monitor 14 mayalso include a speaker 22 to provide an audible sound that may be usedin various other embodiments, such as for example, sounding an audiblealarm in the event that a patient's physiological parameters are notwithin a predefined normal range.

In an embodiment, sensor 12, or the sensor array, may be communicativelycoupled to monitor 14 via a cable 24. However, in other embodiments, awireless transmission device (not shown) or the like may be used insteadof or in addition to cable 24.

In the illustrated embodiment, pulse oximetry system 10 may also includea multi-parameter patient monitor 26. The monitor may be cathode raytube type, a flat panel display (as shown) such as a liquid crystaldisplay (LCD) or a plasma display, or any other type of monitor nowknown or later developed. Multi-parameter patient monitor 26 may beconfigured to calculate physiological parameters and to provide adisplay 28 for information from monitor 14 and from other medicalmonitoring devices or systems (not shown). For example, multiparameterpatient monitor 26 may be configured to display an estimate of apatient's blood oxygen saturation generated by pulse oximetry monitor 14(referred to as an “SpO₂” measurement), pulse rate information frommonitor 14 and blood pressure from a blood pressure monitor (not shown)on display 28.

Monitor 14 may be communicatively coupled to multi-parameter patientmonitor 26 via a cable 32 or 34 that is coupled to a sensor input portor a digital communications port, respectively and/or may communicatewirelessly (not shown). In addition, monitor 14 and/or multi-parameterpatient monitor 26 may be coupled to a network to enable the sharing ofinformation with servers or other workstations (not shown). Monitor 14may be powered by a battery (not shown) or by a conventional powersource such as a wall outlet.

FIG. 2 is a block diagram of a pulse oximetry system, such as pulseoximetry system 10 of FIG. 1, which may be coupled to a patient 40 inaccordance with an embodiment. Certain illustrative components of sensor12 and monitor 14 are illustrated in FIG. 2. Sensor 12 may includeemitter 16, detector 18, and encoder 42. In the embodiment shown,emitter 16 may be configured to emit at least two wavelengths of light(e.g., RED and IR) into a patient's tissue 40. Hence, emitter 16 mayinclude a RED light emitting light source such as RED light emittingdiode (LED) 44 and an IR light emitting light source such as IR LED 46for emitting light into the patient's tissue 40 at the wavelengths usedto calculate the patient's physiological parameters. In one embodiment,the RED wavelength may be between about 600 nm and about 700 nm, and theIR wavelength may be between about 800 nm and about 1000 nm. Inembodiments where a sensor array is used in place of single sensor, eachsensor may be configured to emit a single wavelength. For example, afirst sensor emits only a RED light while a second only emits an IRlight.

It will be understood that, as used herein, the term “light” may referto energy produced by radiative sources and may include one or more ofultrasound, radio, microwave, millimeter wave, infrared, visible,ultraviolet, gamma ray or X-ray electromagnetic radiation. As usedherein, light may also include any wavelength within the radio,microwave, infrared, visible, ultraviolet, or X-ray spectra, and thatany suitable wavelength of electromagnetic radiation may be appropriatefor use with the present techniques. Detector 18 may be chosen to bespecifically sensitive to the chosen targeted energy spectrum of theemitter 16.

In an embodiment, detector 18 may be configured to detect the intensityof light at the RED and IR wavelengths. Alternatively, each sensor inthe array may be configured to detect an intensity of a singlewavelength. In operation, light may enter detector 18 after passingthrough the patient's tissue 40. Detector 18 may convert the intensityof the received light into an electrical signal. The light intensity isdirectly related to the absorbance and/or reflectance of light in thetissue 40. That is, when more light at a certain wavelength is absorbedor reflected, less light of that wavelength is received from the tissueby the detector 18. After converting the received light to an electricalsignal, detector 18 may send the signal to monitor 14, wherephysiological parameters may be calculated based on the absorption ofthe RED and IR wavelengths in the patient's tissue 40.

In an embodiment, encoder 42 may contain information about sensor 12,such as what type of sensor it is (e.g., whether the sensor is intendedfor placement on a forehead or digit) and the wavelengths of lightemitted by emitter 16. This information may be used by monitor 14 toselect appropriate algorithms, lookup tables and/or calibrationcoefficients stored in monitor 14 for calculating the patient'sphysiological parameters.

Encoder 42 may contain information specific to patient 40, such as, forexample, the patient's age, weight, and diagnosis. This information mayallow monitor 14 to determine, for example, patient-specific thresholdranges in which the patient's physiological parameter measurementsshould fall and to enable or disable additional physiological parameteralgorithms. Encoder 42 may, for instance, be a coded resistor whichstores values corresponding to the type of sensor 12 or the type of eachsensor in the sensor array, the wavelengths of light emitted by emitter16 on each sensor of the sensor array, and/or the patient'scharacteristics. In another embodiment, encoder 42 may include a memoryon which one or more of the following information may be stored forcommunication to monitor 14: the type of the sensor 12; the wavelengthsof light emitted by emitter 16; the particular wavelength each sensor inthe sensor array is monitoring; a signal threshold for each sensor inthe sensor array; any other suitable information; or any combinationthereof.

In an embodiment, signals from detector 18 and encoder 42 may betransmitted to monitor 14. In the embodiment shown, monitor 14 mayinclude a general-purpose microprocessor 48 connected to an internal bus50. Microprocessor 48 may be adapted to execute software, which mayinclude an operating system and one or more applications, as part ofperforming the functions described herein. Also connected to bus 50 maybe a read-only memory (ROM) 52, a random access memory (RAM) 54, userinputs 56, display 20, and speaker 22.

RAM 54 and ROM 52 are illustrated by way of example, and not limitation.Any suitable computer-readable media may be used in the system for datastorage. Computer-readable media are capable of storing information thatcan be interpreted by microprocessor 48. This information may be data ormay take the form of computer-executable instructions, such as softwareapplications, that cause the microprocessor to perform certain functionsand/or computer-implemented methods. Depending on the embodiment, suchcomputer-readable media may include computer storage media andcommunication media. Computer storage media may include volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media may include, but is not limited to,RAM, ROM, EPROM, EEPROM, flash memory or other solid state memorytechnology, CD-ROM, DVD, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by components of the system.

In the embodiment shown, a time processing unit (TPU) 58 may providetiming control signals to a light drive circuitry 60, which may controlwhen emitter 16 is illuminated and multiplexed timing for the RED LED 44and the IR LED 46. TPU 58 may also control the gating-in of signals fromdetector 18 through an amplifier 62 and a switching circuit 64. Thesesignals are sampled at the proper time, depending upon which lightsource is illuminated. The received signal from detector 18 may bepassed through an amplifier 66, a low pass filter 68, and ananalog-to-digital converter 70. The digital data may then be stored in aqueued serial module (QSM) 72 (or buffer) for later downloading to RAM54 as QSM 72 fills up. In one embodiment, there may be multiple separateparallel paths having amplifier 66, filter 68, and A/D converter 70 formultiple light wavelengths or spectra received.

In an embodiment, microprocessor 48 may determine the patient'sphysiological parameters, such as SpO₂ and pulse rate, using variousalgorithms and/or look-up tables based on the value of the receivedsignals and/or data corresponding to the light received by detector 18.Signals corresponding to information about patient 40, and particularlyabout the intensity of light emanating from a patient's tissue overtime, may be transmitted from encoder 42 to a decoder 74. These signalsmay include, for example, encoded information relating to patientcharacteristics. Decoder 74 may translate these signals to enable themicroprocessor to determine the thresholds based on algorithms orlook-up tables stored in ROM 52. User inputs 56 may be used to enterinformation about the patient, such as age, weight, height, diagnosis,medications, treatments, and so forth. In an embodiment, display 20 mayexhibit a list of values which may generally apply to the patient, suchas, for example, age ranges or medication families, which the user mayselect using user inputs 56.

The optical signal through the tissue can be degraded by noise, amongother sources. One source of noise is ambient light that reaches thelight detector. Another source of noise is electromagnetic coupling fromother electronic instruments. Movement of the patient also introducesnoise and affects the signal. For example, the contact between thedetector and the skin, or the emitter and the skin, can be temporarilydisrupted when movement causes either to move away from the skin. Inaddition, because blood is a fluid, it responds differently than thesurrounding tissue to inertial effects, thus resulting in momentarychanges in volume at the point to which the oximeter probe is attached.

Noise (e.g., from patient movement) can degrade a pulse oximetry signalrelied upon by a physician, without the physician's awareness. This isespecially true if the monitoring of the patient is remote, the motionis too small to be observed, or the doctor is watching the instrument orother parts of the patient, and not the sensor site. Processing pulseoximetry (i.e., PPG) signals may involve operations that reduce theamount of noise present in the signals or otherwise identify noisecomponents in order to prevent them from affecting measurements ofphysiological parameters derived from the PPG signals.

It will be understood that the present disclosure is applicable to anysuitable signals and that PPG signals are used merely for illustrativepurposes. Those skilled in the art will recognize that the presentdisclosure has wide applicability to other signals including, but notlimited to other biosignals (e.g., electrocardiogram,electroencephalogram, electrogastrogram, electromyogram, heart ratesignals, pathological sounds, ultrasound, or any other suitablebiosignal), dynamic signals, non-destructive testing signals, conditionmonitoring signals, fluid signals, geophysical signals, astronomicalsignals, electrical signals, financial signals including financialindices, sound and speech signals, chemical signals, meteorologicalsignals including climate signals, and/or any other suitable signal,and/or any combination thereof.

In one embodiment, a PPG signal may be transformed using a continuouswavelet transform. Information derived from the transform of the PPGsignal (i.e., in wavelet space) may be used to provide measurements ofone or more physiological parameters.

The continuous wavelet transform of a signal x(t) in accordance with thepresent disclosure may be defined as

$\begin{matrix}{{T\left( {a,b} \right)} = {\frac{1}{\sqrt{a}}{\int_{- \infty}^{+ \infty}{{x(t)}{\psi^{*}\left( \frac{t - b}{a} \right)}d\; t}}}} & (9)\end{matrix}$where φ*(t) is the complex conjugate of the wavelet function φ(t), a isthe dilation parameter of the wavelet and b is the location parameter ofthe wavelet. The transform given by equation (9) may be used toconstruct a representation of a signal on a transform surface. Thetransform may be regarded as a time-scale representation. Wavelets arecomposed of a range of frequencies, one of which may be denoted as thecharacteristic frequency of the wavelet, where the characteristicfrequency associated with the wavelet is inversely proportional to thescale a. One example of a characteristic frequency is the dominantfrequency. Each scale of a particular wavelet may have a differentcharacteristic frequency. The underlying mathematical detail requiredfor the implementation within a time-scale can be found, for example, inPaul S. Addison, The Illustrated Wavelet Transform Handbook (Taylor &Francis Group 2002), which is hereby incorporated by reference herein inits entirety.

The continuous wavelet transform decomposes a signal using wavelets,which are generally highly localized in time. The continuous wavelettransform may provide a higher resolution relative to discretetransforms, thus providing the ability to garner more information fromsignals than typical frequency transforms such as Fourier transforms (orany other spectral techniques) or discrete wavelet transforms.Continuous wavelet transforms allow for the use of a range of waveletswith scales spanning the scales of interest of a signal such that smallscale signal components correlate well with the smaller scale waveletsand thus manifest at high energies at smaller scales in the transform.Likewise, large scale signal components correlate well with the largerscale wavelets and thus manifest at high energies at larger scales inthe transform. Thus, components at different scales may be separated andextracted in the wavelet transform domain. Moreover, the use of acontinuous range of wavelets in scale and time position allows for ahigher resolution transform than is possible relative to discretetechniques.

In addition, transforms and operations that convert a signal or anyother type of data into a spectral (i.e., frequency) domain necessarilycreate a series of frequency transform values in a two-dimensionalcoordinate system where the two dimensions may be frequency and, forexample, amplitude. For example, any type of Fourier transform wouldgenerate such a two-dimensional spectrum. In contrast, wavelettransforms, such as continuous wavelet transforms, are required to bedefined in a three-dimensional coordinate system and generate a surfacewith dimensions of time, scale and, for example, amplitude. Hence,operations performed in a spectral domain cannot be performed in thewavelet domain; instead the wavelet surface must be transformed into aspectrum (i.e., by performing an inverse wavelet transform to convertthe wavelet surface into the time domain and then performing a spectraltransform from the time domain). Conversely, operations performed in thewavelet domain cannot be performed in the spectral domain; instead aspectrum must first be transformed into a wavelet surface (i.e., byperforming an inverse spectral transform to convert the spectral domaininto the time domain and then performing a wavelet transform from thetime domain). Nor does a cross-section of the three-dimensional waveletsurface along, for example, a particular point in time equate to afrequency spectrum upon which spectral-based techniques may be used. Atleast because wavelet space includes a time dimension, spectraltechniques and wavelet techniques are not interchangeable. It will beunderstood that converting a system that relies on spectral domainprocessing to one that relies on wavelet space processing would requiresignificant and fundamental modifications to the system in order toaccommodate the wavelet space processing (e.g., to derive arepresentative energy value for a signal or part of a signal requiresintegrating twice, across time and scale, in the wavelet domain while,conversely, one integration across frequency is required to derive arepresentative energy value from a spectral domain). As a furtherexample, to reconstruct a temporal signal requires integrating twice,across time and scale, in the wavelet domain while, conversely, oneintegration across frequency is required to derive a temporal signalfrom a spectral domain. It is well known in the art that, in addition toor as an alternative to amplitude, parameters such as energy density,modulus, phase, among others may all be generated using such transformsand that these parameters have distinctly different contexts andmeanings when defined in a two-dimensional frequency coordinate systemrather than a three-dimensional wavelet coordinate system. For example,the phase of a Fourier system is calculated with respect to a singleorigin for all frequencies while the phase for a wavelet system isunfolded into two dimensions with respect to a wavelet's location (oftenin time) and scale.

The energy density function of the wavelet transform, the scalogram, isdefined asS(a,b)=|T(a,b)|²  (10)where ‘∥’ is the modulus operator. The scalogram may be rescaled foruseful purposes. One common rescaling is defined as

$\begin{matrix}{{S_{R}\left( {a,b} \right)} = \frac{{{T\left( {a,b} \right)}}^{2}}{a}} & (11)\end{matrix}$and is useful for defining ridges in wavelet space when, for example,the Morlet wavelet is used. Ridges are defined as the locus of points oflocal maxima in the plane. Any reasonable definition of a ridge may beemployed in the method. Also included as a definition of a ridge hereinare paths displaced from the locus of the local maxima. A ridgeassociated with only the locus of points of local maxima in the planeare labeled a “maxima ridge”.

For implementations requiring fast numerical computation, the wavelettransform may be expressed as an approximation using Fourier transforms.Pursuant to the convolution theorem, because the wavelet transform isthe cross-correlation of the signal with the wavelet function, thewavelet transform may be approximated in terms of an inverse FFT of theproduct of the Fourier transform of the signal and the Fourier transformof the wavelet for each required a scale and then multiplying the resultby √{square root over (a)}.

In the discussion of the technology which follows herein, the“scalogram” may be taken to include all suitable forms of rescalingincluding, but not limited to, the original unscaled waveletrepresentation, linear rescaling, any power of the modulus of thewavelet transform, or any other suitable rescaling. In addition, forpurposes of clarity and conciseness, the term “scalogram” shall be takento mean the wavelet transform, T(a,b) itself, or any part thereof. Forexample, the real part of the wavelet transform, the imaginary part ofthe wavelet transform, the phase of the wavelet transform, any othersuitable part of the wavelet transform, or any combination thereof isintended to be conveyed by the term “scalogram”.

A scale, which may be interpreted as a representative temporal period,may be converted to a characteristic frequency of the wavelet function.The characteristic frequency associated with a wavelet of arbitrary ascale is given by

$\begin{matrix}{f = \frac{f_{c}}{a}} & (12)\end{matrix}$where f_(c), the characteristic frequency of the mother wavelet (i.e.,at a=1), becomes a scaling constant and f is the representative orcharacteristic frequency for the wavelet at arbitrary scale a.

Any suitable wavelet function may be used in connection with the presentdisclosure. One of the most commonly used complex wavelets, the Morletwavelet, is defined as:ψ(t)=π^(−1/4)(e ^(i2πf) ⁰ ^(t) −e ^(−(2πf) ⁰ ⁾ ² ^(/2))e ^(−t) ²^(/2)  (13)where f₀ is the central frequency of the mother wavelet. The second termin the parenthesis is known as the correction term, as it corrects forthe non-zero mean of the complex sinusoid within the Gaussian window. Inpractice, it becomes negligible for values of f₀>>0 and can be ignored,in which case, the Morlet wavelet can be written in a simpler form as

$\begin{matrix}{{\psi(t)} = {\frac{1}{\pi^{1\text{/}4}}e^{i\; 2\;\pi\; f_{0}t}e^{{- t^{2}}/2}}} & (14)\end{matrix}$

This wavelet is a complex wave within a scaled Gaussian envelope. Whileboth definitions of the Morlet wavelet are included herein, the functionof equation (14) is not strictly a wavelet as it has a non-zero mean(i.e., the zero frequency term of its corresponding energy spectrum isnon-zero). However, it will be recognized by those skilled in the artthat equation (14) may be used in practice with f₀>>0 with minimal errorand is included (as well as other similar near wavelet functions) in thedefinition of a wavelet herein. A more detailed overview of theunderlying wavelet theory, including the definition of a waveletfunction, can be found in the general literature. Discussed herein ishow wavelet transform features may be extracted from the waveletdecomposition of signals. For example, wavelet decomposition of PPGsignals may be used to provide clinically useful information within amedical device.

Pertinent repeating features in a signal give rise to a time-scale bandin wavelet space or a rescaled wavelet space. For example, the pulsecomponent of a PPG signal produces a dominant band in wavelet space ator around the pulse frequency. FIGS. 3(a) and (b) show two views of anillustrative scalogram derived from a PPG signal, according to anembodiment. The figures show an example of the band caused by the pulsecomponent in such a signal. The pulse band is located between the dashedlines in the plot of FIG. 3(a). The band is formed from a series ofdominant coalescing features across the scalogram. This can be clearlyseen as a raised band across the transform surface in FIG. 3(b) locatedwithin the region of scales indicated by the arrow in the plot(corresponding to 60 beats per minute). The maxima of this band withrespect to scale is the ridge. The locus of the ridge is shown as ablack curve on top of the band in FIG. 3(b). By employing a suitablerescaling of the scalogram, such as that given in equation (11), theridges found in wavelet space may be related to the instantaneousfrequency of the signal. In this way, the pulse rate may be obtainedfrom the PPG signal. Instead of rescaling the scalogram, a suitablepredefined relationship between the scale obtained from the ridge on thewavelet surface and the actual pulse rate may also be used to determinethe pulse rate.

By mapping the time-scale coordinates of the pulse ridge onto thewavelet phase information gained through the wavelet transform,individual pulses may be captured. In this way, both times betweenindividual pulses and the timing of components within each pulse may bemonitored and used to detect heart beat anomalies, measure arterialsystem compliance, or perform any other suitable calculations ordiagnostics. Alternative definitions of a ridge may be employed.Alternative relationships between the ridge and the pulse frequency ofoccurrence may be employed.

As discussed above, pertinent repeating features in the signal give riseto a time-scale band in wavelet space or a rescaled wavelet space. For aperiodic signal, this band remains at a constant scale in the time-scaleplane. For many real signals, especially biological signals, the bandmay be non-stationary; varying in scale, amplitude, or both over time.FIG. 3(c) shows an illustrative schematic of a wavelet transform of asignal containing two pertinent components leading to two bands in thetransform space, according to an embodiment. These bands are labeledband A and band B on the three-dimensional schematic of the waveletsurface. In this embodiment, the band ridge is defined as the locus ofthe peak values of these bands with respect to scale. For purposes ofdiscussion, it may be assumed that band B contains the signalinformation of interest. This will be referred to as the “primary band”.In addition, it may be assumed that the system from which the signaloriginates, and from which the transform is subsequently derived,exhibits some form of coupling between the signal components in band Aand band B. When noise or other erroneous features are present in thesignal with similar spectral characteristics of the features of band Bthen the information within band B can become ambiguous (i.e., obscured,fragmented or missing). In this case, the ridge of band A may befollowed in wavelet space and extracted either as an amplitude signal ora scale signal which will be referred to as the “ridge amplitudeperturbation” (RAP) signal and the “ridge scale perturbation” (RSP)signal, respectively. The RAP and RSP signals may be extracted byprojecting the ridge onto the time-amplitude or time-scale planes,respectively. The top plots of FIG. 3(d) show a schematic of the RAP andRSP signals associated with ridge A in FIG. 3(c). Below these RAP andRSP signals are schematics of a further wavelet decomposition of thesenewly derived signals. This secondary wavelet decomposition allows forinformation in the region of band B in FIG. 3(c) to be made available asband C and band D. The ridges of bands C and D may serve asinstantaneous time-scale characteristic measures of the signalcomponents causing bands C and D. This technique, which will be referredto herein as secondary wavelet feature decoupling (SWFD), may allowinformation concerning the nature of the signal components associatedwith the underlying physical process causing the primary band B (FIG.3(c)) to be extracted when band B itself is obscured in the presence ofnoise or other erroneous signal features.

In some instances, an inverse continuous wavelet transform may bedesired, such as when modifications to a scalogram (or modifications tothe coefficients of a transformed signal) have been made in order to,for example, remove artifacts. In one embodiment, there is an inversecontinuous wavelet transform which allows the original signal to berecovered from its wavelet transform by integrating over all scales andlocations, a and b:

$\begin{matrix}{{x(t)} = {\frac{1}{C_{g}}{\int_{- \infty}^{\infty}{\int_{0}^{\infty}{{T\left( {a,b} \right)}\frac{1}{\sqrt{a}}{\psi\left( \frac{t - b}{a} \right)}\ \frac{d\; a\ d\; b}{a^{2}}}}}}} & (15)\end{matrix}$which may also be written as:

$\begin{matrix}{{x(t)} = {\frac{1}{C_{g}}{\int_{- \infty}^{\infty}{\int_{0}^{\infty}{{T\left( {a,b} \right)}{\psi_{a,b}(t)}\ \frac{d\; a\ d\; b}{a^{2}}}}}}} & (16)\end{matrix}$where C_(g) is a scalar value known as the admissibility constant. It iswavelet type dependent and may be calculated from:

$\begin{matrix}{C_{g} = {\int_{0}^{\infty}{\frac{{{\hat{\psi}(f)}}^{2}}{f}\ d\; f}}} & (17)\end{matrix}$

FIG. 3(e) is a flow chart of illustrative steps that may be taken toperform an inverse continuous wavelet transform in accordance with theabove discussion. An approximation to the inverse transform may be madeby considering equation (15) to be a series of convolutions acrossscales. It shall be understood that there is no complex conjugate here,unlike for the cross correlations of the forward transform. As well asintegrating over all of a and b for each time t, this equation may alsotake advantage of the convolution theorem which allows the inversewavelet transform to be executed using a series of multiplications. FIG.3(f) is a flow chart of illustrative steps that may be taken to performan approximation of an inverse continuous wavelet transform. It will beunderstood that any other suitable technique for performing an inversecontinuous wavelet transform may be used in accordance with the presentdisclosure.

FIG. 4 is an illustrative continuous wavelet processing system inaccordance with an embodiment. In this embodiment, input signalgenerator 410 generates an input signal 416. As illustrated, inputsignal generator 410 may include oximeter 420 coupled to sensor 418,which may provide as input signal 416, a PPG signal. It will beunderstood that input signal generator 410 may include any suitablesignal source, signal generating data, signal generating equipment, orany combination thereof to produce signal 416. Signal 416 may be anysuitable signal or signals, such as, for example, biosignals (e.g.,electrocardiogram, electroencephalogram, electrogastrogram,electromyogram, heart rate signals, pathological sounds, ultrasound, orany other suitable biosignal), dynamic signals, non-destructive testingsignals, condition monitoring signals, fluid signals, geophysicalsignals, astronomical signals, electrical signals, financial signalsincluding financial indices, sound and speech signals, chemical signals,meteorological signals including climate signals, and/or any othersuitable signal, and/or any combination thereof.

In this embodiment, signal 416 may be coupled to processor 412.Processor 412 may be any suitable software, firmware, and/or hardware,and/or combinations thereof for processing signal 416. For example,processor 412 may include one or more hardware processors (e.g.,integrated circuits), one or more software modules, computer-readablemedia such as memory, firmware, or any combination thereof. Processor412 may, for example, be a computer or may be one or more chips (i.e.,integrated circuits). Processor 412 may perform the calculationsassociated with the continuous wavelet transforms of the presentdisclosure as well as the calculations associated with any suitableinterrogations of the transforms. Processor 412 may perform any suitablesignal processing of signal 416 to filter signal 416, such as anysuitable band-pass filtering, adaptive filtering, closed-loop filtering,and/or any other suitable filtering, and/or any combination thereof.

Processor 412 may be coupled to one or more memory devices (not shown)or incorporate one or more memory devices such as any suitable volatilememory device (e.g., RAM, registers, etc.), non-volatile memory device(e.g., ROM, EPROM, magnetic storage device, optical storage device,flash memory, etc.), or both. The memory may be used by processor 412to, for example, store data corresponding to a continuous wavelettransform of input signal 416, such as data representing a scalogram. Inone embodiment, data representing a scalogram may be stored in RAM ormemory internal to processor 412 as any suitable three-dimensional datastructure such as a three-dimensional array that represents thescalogram as energy levels in a time-scale plane. Any other suitabledata structure may be used to store data representing a scalogram.

Processor 412 may be coupled to output 414. Output 414 may be anysuitable output device such as, for example, one or more medical devices(e.g., a medical monitor that displays various physiological parameters,a medical alarm, or any other suitable medical device that eitherdisplays physiological parameters or uses the output of processor 412 asan input), one or more display devices (e.g., monitor, PDA, mobilephone, any other suitable display device, or any combination thereof),one or more audio devices, one or more memory devices (e.g., hard diskdrive, flash memory, RAM, optical disk, any other suitable memorydevice, or any combination thereof), one or more printing devices, anyother suitable output device, or any combination thereof.

It will be understood that system 400 may be incorporated into system 10(FIGS. 1 and 2) in which, for example, input signal generator 410 may beimplemented as parts of sensor 12 and monitor 14 and processor 412 maybe implemented as part of monitor 14.

FIG. 5 shows an illustrative system 500 in which SpO₂ processor 508 maydetermine oxygen saturation from PPG signals 502 (e.g., red and infraredPPG signals). Wavelet processor 504 and spectral processor 506 may usewavelet transform techniques and spectral techniques, respectively, toprocess PPG signals 502 to generate, for example, coefficients. SpO₂processor 508 may determine oxygen saturation by taking ratios ofcoefficients or generating Lissajous figures from the coefficients. SpO₂processor 508 may calculate oxygen saturation using coefficients fromwavelet processor 504, spectral processor 506, or a combination thereof.System 500 may be implemented within continuous wavelet processingsystem 400 (FIG. 4) or within any other suitable systems. As used hereinthe PPG signal may be a raw intensity signal or a scaled or normalizedversion thereof.

Wavelet processor 504 may perform one or more continuous wavelettransforms of PPG signals 502 in accordance with the present disclosure.Wavelet processor 504 may generate a scalogram corresponding to a redPPG signal and a scalogram corresponding to an infrared PPG signal. Oneor both scalograms may be analyzed to identify a pulse band.Alternatively, a pulse rate may be determined using other processing ofone or more PPG signals and may be mapped onto the scalograms based onthe characteristic frequencies of scales. As described above, ascalogram generated from a continuous wavelet transform of a PPG signalgenerally contains a band of scales corresponding to the pulsecomponents of the PPG signal, a band of scales corresponding to thebreathing components of the PPG signal, and one or more other bands ofscales that correspond to other components such as noise and artifacts.In some embodiments, wavelet processor 504 may remove the bands ofscales from the scalograms that do not correspond to the pulsecomponents.

With the pulse band identified, wavelet processor 504 may locate theridge of the pulse band. The pulse band in a red PPG signal and aninfrared signal may be located in the same or similar band of scales.Coefficients corresponding to the amplitudes of the pulse ridges in thescalograms that correspond to the red and infrared PPG signal may beoutputted to SpO₂ processor 508 for further processing. In addition tothe foregoing or as an alternative, wavelet processor 504 may not needto identify the pulse band and may instead output coefficientscorresponding to the amplitudes of the scalograms across a range ofscales over time (e.g., a predetermined time period).

In an embodiment, wavelet processor 504 may filter PPG signals 502 usingone or more suitable filters (not shown) to reduce noise or artifactspresent in PPG signals 502. Wavelet processor 504 may filter PPG signals502 before or after performing the continuous wavelet transforms.Furthermore, the one or more filters may also be used to reduce featureswithin PPG signal 502 that may not contain desired information. Forexample, a band-pass filter may be applied to PPG signal 502 toattenuate frequency components outside of a desired frequency range.Generating scalograms by performing continuous wavelet transforms onfiltered PPG signals may enhance the band of scales corresponding to thepulse components while preferably reducing or eliminating otherundesired bands of scales. As used herein, the term “filter” shall referto any suitable type of filter or processor capable of filtering orprocessing a signal to either generate a filtered signal or to extractdata from the signal.

Spectral processor 504 may also be used in accordance with the presentdisclosure to generate coefficients from PPG signals 502. Spectralprocessor 504 may perform one or more frequency transforms (e.g.,Fourier transforms) on PPG signals 502 in order to transform the PPGsignals from the time-domain into a frequency domain representation ofthe PPG signals. Any suitable frequency transform functions orcombination of frequency transform functions may be used by spectralprocessor 504 including, for example, continuous Fourier transforms,discreet Fourier transforms, fast Fourier transforms (FFT), and forwardFourier transforms. The transformed PPG signals may represent amplitudesof the signals as a function of frequency. Pertinent repeating featuresin PPG signals 502 may give rise to peaks within the transformed PPGsignals corresponding to the frequencies of those features. Accordingly,the pulse component of PPG signals 502 may produce a peak in thetransformed PPG signals at or around the pulse frequency. Othercomponents of PPG signals 502 including breathing components of the PPGsignal as well as noise or artifacts may also produce peaks in thetransformed PPG signal.

Spectral processor 506 may output coefficients of transformed PPGsignals 502 at the frequency that has the largest peak in thetransforms. In other embodiments, spectral processor 506 may outputcoefficients corresponding to the frequency of other peaks or locationsin the transformed PPG signals or the coefficients corresponding to arange of frequencies in the transformed PPG signals.

Spectral processor 506 may filter PPG signals 502 using one or moresuitable filters (not shown) before or after performing the spectraltransforms to reduce noise or artifacts present in PPG signals 502.Furthermore, the one or more filters may also be used to reduce featureswithin PPG signal 502 that do not contain desired information. Filteringthe PPG signal in this manner may enhance the peaks in the transformedPPG signal corresponding to desired components while preferably reducingor eliminating other peaks.

SpO₂ processor 508 may determine oxygen saturation from PPG signals 502based on coefficients generated by wavelet processor 504 and spectralprocessor 506. SpO₂ processor 508 may calculate oxygen saturation fromthe coefficients received from wavelet processor 504 using any suitablemethod. In one suitable approach, if the received coefficients representamplitude values of the pulse ridges in the two scalograms, SpO₂processor 508 may take a ratio of the amplitude values corresponding tothe red PPG signal over the amplitude values corresponding to theinfrared PPG signal. SpO₂ processor 508 may input the ratio into alookup table or an equation to determine oxygen saturation. In anothersuitable approach, if the received coefficients represent amplitudes fora range of scales over a period of time, SpO₂ processor 508 may generateone or more Lissajous figures to determine oxygen saturation. Forexample, SpO₂ processor 508 may select an optimal Lissajous figure,determine a slope component from the selected Lissajous figure, andinput the slope component into a lookup table or equation to determineoxygen saturation. In another suitable approach, the SpO₂ processor 508may determine oxygen saturation from coefficients from wavelet processor504 using the techniques describes in Addison et al. U.S. PatentPublication No. 2006/0258921, published Nov. 16, 2006, which isincorporated by reference herein in its entirety.

SpO₂ processor 508 may calculate oxygen saturation from the coefficientsreceived from spectral processor 506 using any suitable method. In onesuitable approach, SpO₂ processor 508 may take a ratio of the receivedcoefficient at a particular frequency (e.g., a frequency correspondingto the pulse rate). The SpO₂ processor 508 may input the ratio into alookup table or an equation to determine oxygen saturation.

In an embodiment, SpO₂ processor 508 may calculate one oxygen saturationfrom the coefficients received from wavelet processor 504, andseparately calculate another oxygen saturation from the coefficientsreceived from spectral processor 506. The two oxygen saturationcalculations may then be combined to generate an optimal oxygensaturation. The optimal oxygen saturation may be combined by using anaverage or a weighted average of the two oxygen saturation calculations.

In an embodiment, SpO₂ processor 508 may compare the two oxygensaturation calculations and select one of the two oxygen saturationcalculations as the optimal oxygen saturation measurement. For example,if the two oxygen saturation calculations differ by more than apredetermined amount, SpO₂ processor 508 may select only one of the twooxygen saturation calculation as the optimal oxygen saturationmeasurement. SpO₂ processor 508 may select the oxygen saturationcalculation with a more likely value, with a more stable value, or witha value closer to a previously determined oxygen saturation measurement.Alternatively, SpO₂ processor 508 may select one of the oxygensaturation calculations based on the source of its coefficients. Forexample, oxygen saturation calculated using coefficients received fromwavelet processor 504 may be given priority over the oxygen saturationcalculated using coefficients received from spectral processor 506 orvise versa.

Alternatively, when the two oxygen saturation calculations differ byless than a predetermined amount, SpO₂ processor 508 may select only oneof the oxygen saturation calculations as the optimal measurement.Similar to the foregoing, the oxygen saturation calculation may beselected based on its value or based on its source.

Wavelet processor 504 and spectral processor 506 may provide confidencevalues associated with their respective coefficients. A high confidencevalue may reflect a greater accuracy in determining oxygen saturation,while a low confidence value may reflect a lower accuracy. For example,the confidence value generated by wavelet processor 504 may bedetermined based on the quality the band of scales corresponding to thecomponents of the PPG signals (e.g., the pulse band) and the presence,proximity, and quality of other bands of scales. Similarly, theconfidence value generated by spectral processor 506 may be determinedbased on the size of peaks (e.g., corresponding to the pulse rate) andthe presence, proximity, and values of other peaks. SpO₂ processor 508may compare the confidence values associated with the oxygen saturationcalculations. In an embodiment, SpO₂ processor 508 may select the oxygensaturation with the higher confidence value. In other embodiments SpO₂processor 508 may combine the two oxygen saturation calculations byassigning weights based on at least in part on the confidence values.

Whereas wavelet processor 504 and spectral processor 506 have beendescribed herein as generating coefficients independently, it should beunderstood that information may be passed between wavelet processor 504and spectral processor 506 in order to improve the accuracy and speed ofgenerating the wavelet and spectral transforms. For example, spectralprocessor 506 may provide wavelet processor 504 the frequency value of apeak identified to be associated with a pulse rate. Wavelet processor504 may use this frequency to determine which scale values to examine ina scalogram, for example those of a similar characteristic frequency maybe considered. Similarly, wavelet processor 504 may provide informationfrom scales identified from a scalogram, for example intermittencyinformation, to spectral processor 506 to help spectral processor 506select the correct peak. Intermittency information may indicate, forexample, whether the energy in a spectrum manifests as a quick burst(resembling transient noise) or is spread evenly in time (like acontinuous pulse).

The SpO₂ processor 508 has been described herein as calculating anoptimal oxygen saturation from two oxygen saturation calculations. Itshould be understood that SpO₂ processor 508 may also calculate anoptimal oxygen saturation using more than two oxygen saturationcalculations using the techniques discussed above. For example, SpO₂processor 508 may calculate two or more oxygen saturations from theoutput received from wavelet processor 504 (e.g., one from a ratio ofthe amplitudes from the pulse ridges and another from a Lissajousfigure) and two or more oxygen saturations from the output received fromspectral processor 506.

The wavelet processor 504 and spectral processor 506 have been describedas outputting coefficients to SpO₂ processor 508. It should beunderstood that wavelet processor 504 may output all or portions ofwavelet transforms or a scalograms to SpO₂ processor 508 for furtherprocessing as discussed above. Similarly, it will be understood thatspectral processor 506 may output all or portions of spectral transformsto SpO₂ processor 508 for further processing as discussed above.

FIG. 6 is a flowchart of an illustrative process for identifying oxygensaturation from a PPG signal using continuous wavelet transforms andspectral transforms. Process 600 may begin at step 602. At step 604, afirst PPG signal and a second PPG signal are received. At step 606 a,the first PPG signal may be transformed using a continuous wavelettransform to generate a first wavelet transformed signal and the secondPPG signal may be transformed using a continuous wavelet transform togenerate a second wavelet transformed signal. At step 606 b, the firstPPG signal may be transformed using a spectral transform to generate afirst spectral transformed signal and the second PPG signal may betransformed using a spectral transform to generate a second spectraltransformed signal. Then, at step 608 a a first oxygen saturation may bedetermined from the first and the second wavelet transformed signals andat step 608 b a second oxygen saturation may be determined from thefirst and the second spectral transformed signals. While steps 606 a-608a and 606 b-608 b are illustrated as being performed substantially inparallel (e.g., by wavelet processor 504 and spectral processor 506(FIG. 5)), it should be understood that these steps may also beperformed serially. At step 610, optimal oxygen saturation may bedetermined based at least in part on the first oxygen saturation and thesecond oxygen saturation. Process 600 may then advance to step 612 andend.

FIG. 7 is a flowchart of another illustrative process for identifyingoxygen saturation from a PPG signal using continuous wavelet transformsand spectral transforms. Process 700 may begin at step 702. At step 704,a first PPG signal and a second PPG signal are received. At step 706,the first PPG signal may be transformed using a spectral transform togenerate a first spectral transformed signal and the second PPG signalmay be transformed using a spectral transform to generate a secondspectral transformed signal. At step 708 a frequency region associatedwith a pulse rate of the spectral tranformed PPG signals is identified.For example, spectral processor 506 (FIG. 5) may identify one or morefrequency regions of interest within the spectral tranformed signals. Atstep 710 the first and the second PPG signals may be transformed using acontinuous wavelet transform at a scale corresponding to the identifiedfrequency regions and at step 712 oxygen saturation may be determinedfrom the wavelet transformed signals. For example, Wavelet processor 504(FIG. 5) may provide a more accurate calculation of oxygen saturationusing the frequency regions of interest provided by spectral processor506. Process 700 may then advance to step 712 and end.

The foregoing is merely illustrative of the principles of thisdisclosure and various modifications can be made by those skilled in theart without departing from the scope and spirit of the disclosure. Thefollowing claims may also describe various aspects of the disclosure.

What is claimed is:
 1. A method for determining oxygen saturation fromPPG signals, comprising: receiving a first PPG signal and a second PPGsignal, as measured by a sensor comprising an emitter and a detector,wherein the emitter is configured to emit light having two or morewavelengths into a patient's tissue and the detector is configured toreceive at least a portion of the emitted light to generate the firstand the second PPG signals; using a processor to: perform a spectraltransform of the first and the second PPG signals to produce a firstspectral transformed signal and a second spectral transformed signal;identify from the first spectral transformed signal and the secondspectral transformed signal a frequency region associated with a pulserate of the first and the second PPG signals; perform a continuouswavelet transform of the first and the second PPG signals at a scalecorresponding to the identified frequency region to produce a firstwavelet transformed signal and a second wavelet transformed signal; anddetermine oxygen saturation based at least in part upon the pulse bandof one or both of the first wavelet transformed signal or the secondwavelet transformed signal.
 2. The method of claim 1, whereinidentifying the frequency region associated with a pulse rate of thefirst and the second PPG signals comprises identifying peaks in thefirst and the second spectral transformed signals.
 3. The method ofclaim 1, wherein determining oxygen saturation comprises determiningoxygen saturation over time.
 4. The method of claim 1, comprising usingthe processor to filter the first and the second PPG signals beforeperforming the continuous wavelet transform.
 5. The method of claim 1,comprising using the processor to filter the first and the second PPGsignals after performing the continuous wavelet transform.
 6. The methodof claim 1, comprising using the processor to identify scales from thefirst and the second wavelet transformed signals.
 7. The method of claim6, wherein the processor selects a peak corresponding to the pulse ratebased on the identified scales.
 8. The method of claim 1, comprisingusing the processor to identify the pulse band.
 9. The method of claim1, comprising using the processor to identify a ridge in the pulse band,wherein oxygen saturation is determined based on coefficientscorresponding to an amplitude of the ridge.
 10. A computer-readablemedium for use in determining oxygen saturation from a first PPG signaland a second PPG signal, the computer-readable medium having computerprogram instructions recorded thereon for: performing a spectraltransform of the first and the second PPG signals to produce a first anda second spectral transformed signals, wherein the first and the secondPPG signals correspond to an intensity of light of a portion of lightemitted from an emitter and detected by a detector configured togenerate the first and the second PPG signals, wherein the emitter andthe detector are part of a pulse oximetry sensor; identifying from thefirst and the second spectral transformed signals a frequency regionassociated with a pulse rate of the first and the second PPG signals;performing a continuous wavelet transform of the first and the secondPPG signals at a scale corresponding to the identified frequency regionto produce a first wavelet transformed signal and a second wavelettransformed signal; and determining oxygen saturation based at least inpart on a pulse band of the first or the second wavelet transformedsignals.
 11. The computer-readable medium of claim 10, wherein theinstructions are configured to filter the first and the second PPGsignals before performing the continuous wavelet transform.
 12. Thecomputer-readable medium of claim 10, wherein identifying the frequencyregion associated with a pulse rate of the first and the second PPGsignals comprises identifying peaks in the spectral transformed signals.13. The computer-readable medium of claim 10, wherein determining oxygensaturation comprises determining oxygen saturation over time.
 14. Thecomputer-readable medium of claim 10, wherein the instructions areconfigured to filter the first and the second PPG signals afterperforming the continuous wavelet transform.
 15. The computer-readablemedium of claim 10, wherein the instructions are configured to identifyscales from the first and the second wavelet transformed signals. 16.The computer-readable medium of claim 10, wherein the instructions areconfigured to select a peak corresponding to the pulse rate based on theidentified scales.
 17. A method for determining oxygen saturation fromPPG signals, comprising: receiving a first PPG signal and a second PPGsignal, as measured by a sensor comprising an emitter and a detector,wherein the emitter is configured to emit light having two or morewavelengths into a patient's tissue and the detector is configured toreceive at least a portion of the emitted light to generate the firstand the second PPG signals based on an intensity of the portion of theemitted light; using a processor to: perform a continuous wavelettransform of the first PPG signal to produce a first wavelet transformedsignal and of the second PPG signal to produce a second wavelettransformed signal; perform a spectral transform of the first PPG signalto generate a first spectral transformed signal and of the second PPGsignal to produce a second spectral transformed signal; identify a pulseband based on one or both of the first wavelet transformed signal or thesecond wavelet transformed signal, wherein the pulse band corresponds topulse components of one or both of the first PPG signal or the secondPPG signal; and determine oxygen saturation based at least in part uponthe pulse band.
 18. The method of claim 17, comprising using theprocessor to identify a ridge in the identified pulse band, wherein theridge is associated with an instantaneous frequency of one or both offirst PPG signal or the second PPG signal.
 19. The method of claim 15,comprising using the pulse band to identify coefficients correspondingto an amplitude of a pulse band ridge and using the coefficients todetermine the oxygen saturation.